Volume acquisition method for object in ultrasonic image and related ultrasonic system

ABSTRACT

An object volume acquisition method of an ultrasonic image, for a probe of an ultrasonic system is disclosed. The volume acquisition method of the object in the ultrasonic image includes collecting, by the probe, a plurality of two-dimensional ultrasonic images; obtaining the plurality of two-dimensional ultrasonic images, an offset angle, a rotation axis and a frequency of the probe corresponding to the plurality of two-dimensional ultrasonic images; segmenting a first image including an ultrasonic image object from each two-dimensional ultrasonic image of the plurality of two-dimensional ultrasonic images based on a deep learning structure; determining a contour of the ultrasonic image object; reconstructing a three-dimensional model corresponding to the ultrasonic image object according to the contour of the ultrasonic image object corresponding to the each two-dimensional ultrasonic image; and calculating a volume of the ultrasonic image object according to the three-dimensional model corresponding to the ultrasonic image object.

BACKGROUND OF THE INVENTION 1. Field of the Invention

The present invention relates to a volume acquisition method for objectin ultrasonic image and related ultrasonic system, and moreparticularly, to a volume acquisition method for object in ultrasonicimage and related ultrasonic system capable of establishing athree-dimensional model with a deep learning structure to efficientlyand accurately calculate the volume of the object in the ultrasonicimage.

2. Description of the Prior Art

Conventional imaging techniques in the medical field, e.g. magneticresonance imaging (MRI), computed tomography (CT) scanning andultrasonic three-dimensional imaging techniques, can clearly andefficiently obtain images with non-invasive methods to preciselycalculate or estimate a volume of an object in the images, and thus theconventional imaging techniques are widely utilized in medicalexaminations. The conventional medical techniques estimate the volume ofthe object in an ultrasonic image based on length, width, height of theobject. However, an error occurs when the above conventional medicaltechniques perform an image capturing, an image segmentation and avolume calculation, e.g. a bladder of a subject is deformed because ofthe extrusion of a device, the bladder extents towards both sides andsqueezes adjacent organs, which makes a shape of the bladder a non-ovalshape, and causes error in volume estimation. A urine volume in thebladder cannot be precisely estimated accordingly. Therefore,improvements are necessary to the conventional technique.

SUMMARY OF THE INVENTION

The present invention provides a volume acquisition method for an objectin ultrasonic image and related ultrasonic system to accuratelycalculate the volume of the ultrasonic image object based on deeplearning structure.

An embodiment of the present invention discloses an object volumeacquisition method of an ultrasonic image, for a probe of an ultrasonicsystem, wherein the volume acquisition method of the object in theultrasonic image comprises collecting, by the probe, a plurality oftwo-dimensional ultrasonic images; obtaining the plurality oftwo-dimensional ultrasonic images, an offset angle, a rotation axis anda frequency of the probe corresponding to the plurality oftwo-dimensional ultrasonic images; segmenting a first image including anultrasonic image object from each two-dimensional ultrasonic image ofthe plurality of two-dimensional ultrasonic images based on a deeplearning structure; determining a contour of the ultrasonic image objectaccording to the first image corresponding to the each two-dimensionalultrasonic image; reconstructing a three-dimensional model correspondingto the ultrasonic image object according to the contour of theultrasonic image object corresponding to the each two-dimensionalultrasonic image; and calculating a volume of the ultrasonic imageobject according to the three-dimensional model corresponding to theultrasonic image object.

Another embodiment of the present invention discloses an ultrasonicsystem, for calculating a volume of ultrasonic image object, comprises aprobe, configured to collect a plurality of two-dimensional ultrasonicimages; and a processor, configured to obtain the plurality oftwo-dimensional ultrasonic images, an offset angle, a rotation axis anda frequency of the probe corresponding to the plurality oftwo-dimensional ultrasonic images, segment a first image including anultrasonic image object from each two-dimensional ultrasonic image ofthe plurality of two-dimensional ultrasonic images based on a deeplearning structure, determine a contour of the ultrasonic image objectaccording to the first image corresponding to the each two-dimensionalultrasonic image, reconstruct a three-dimensional model corresponding tothe ultrasonic image object according to the contour of the ultrasonicimage object corresponding to the each two-dimensional ultrasonic image,and calculate a volume of the ultrasonic image object according to thethree-dimensional model corresponding to the ultrasonic image object.

These and other objectives of the present invention will no doubt becomeobvious to those of ordinary skill in the art after reading thefollowing detailed description of the preferred embodiment that isillustrated in the various figures and drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic diagram of an ultrasonic system according to anembodiment of the present invention.

FIG. 2 is a schematic diagram of a volume acquisition method for anultrasonic image object according to an embodiment of the presentinvention.

FIG. 3 is a schematic diagram of a contour determination methodaccording to an embodiment of the present invention.

FIG. 4 and FIG. 5 are schematic diagrams of a contour determinationmethod according to another embodiment of the present invention.

FIG. 6 is a schematic diagram of a three-dimensional modeling methodaccording to an embodiment of the present invention.

FIG. 7 is a schematic diagram of a three-dimensional inner interpolationaccording to an embodiment of the present invention.

FIG. 8 is a schematic diagram of a three-dimensional externalinterpolation according to an embodiment of the present invention.

DETAILED DESCRIPTION

Refer to FIG. 1, which is a schematic diagram of an ultrasonic system 10according to an embodiment of the present invention. The ultrasonicsystem 10 includes a probe 102 and a processor 104. The ultrasonicsystem 10 is configured to determine a volume of an ultrasonic imageobject. The probe 102 is configured to obtain a plurality oftwo-dimensional ultrasonic images. The ultrasonic system 10 may beutilized for examinations in the medical field, which measures orestimates a volume of a human organ, e.g. a uterus, a prostate or abladder, with an ultrasonic reflection method. In an embodiment, theprobe 102 includes a positioning device for collecting thetwo-dimensional ultrasonic images and corresponding positioninginformation. The processor 104 is configured to obtain thetwo-dimensional ultrasonic images and the corresponding positioninginformation from the probe 102, e.g. an offset angle, a rotation axisand a frequency of the probe, i.e. the probe 102 may be utilized forscanning the human organs from different offset angles to obtain thetwo-dimensional ultrasonic images. The processor 104 is configured tosegment a first image including an ultrasonic image object from eachtwo-dimensional ultrasonic image of the two-dimensional ultrasonicimages based on a deep learning structure, and calculate a volume of theultrasonic image object according to the first image corresponding toeach of the two-dimensional ultrasonic images and a three-dimensionalmodel corresponding to the ultrasonic image object. Therefore, theaccuracy of the measured volume of the ultrasonic image object isimproved according to the deep learning structure and thethree-dimensional model of the ultrasonic system 10 according to anembodiment of the present invention.

In detail, please refer to FIG. 2, which is a schematic diagram of avolume acquisition method 20 for the ultrasonic image object accordingto an embodiment of the present invention. As shown in FIG. 2, thevolume acquisition method 20 for the ultrasonic image object includesthe following steps:

Step 202: Start.

Step 204: Obtain the two-dimensional ultrasonic images and the offsetangle, the rotation axis and the frequency of the probe corresponding tothe two-dimensional ultrasonic images.

Step 206: Segment the first image including the ultrasonic image objectfrom each two-dimensional ultrasonic image of the two-dimensionalultrasonic images based on the deep learning structure.

Step 208: Determine a contour of the ultrasonic image object accordingto the first image corresponding to each of the two-dimensionalultrasonic images.

Step 210: Reconstruct the three-dimensional model corresponding to theultrasonic image object, according to the contour of the ultrasonicimage object corresponding to each of the two-dimensional ultrasonicimages.

Step 212: Calculate the volume of the ultrasonic image object accordingto the three-dimensional model corresponding to the ultrasonic imageobject.

Step 214: End.

First, in step 204, the ultrasonic system 10 may utilize the probe 102to collect the two-dimensional ultrasonic images and the offset angle,the rotation axis and the frequency of the probe 102 corresponding tothe two-dimensional ultrasonic images to increase characteristics of theultrasonic image object in the two-dimensional ultrasonic images. Morespecifically, the offset angle, the rotation axis and the frequency ofthe probe 102 may be utilized for establishing the three-dimensionalmodel of the ultrasonic image object.

Then, in step 206, the ultrasonic system 10 segments the first imageincluding the ultrasonic image object from each two-dimensionalultrasonic image of the two-dimensional ultrasonic images based on thedeep learning structure. In an embodiment, the ultrasonic system 10according to an embodiment of the present invention may segment thefirst image including the ultrasonic image object from each of thetwo-dimensional ultrasonic images based on a deep learning structure forsemantic segmentation of in a U-Net network structure, wherein the deeplearning structure for semantic segmentation classifies each of pixelsin a given image to obtain a target image.

In detail, the ultrasonic system 10 according to an embodiment of thepresent invention adopts the deep learning structure and a self-learningmethod to detect a preliminary contour and a location of the ultrasonicimage object according to the two-dimensional ultrasonic images and theprobe corresponding to the two-dimensional ultrasonic images, so as toposition the ultrasonic image object and segment the first imageincluding the ultrasonic image object in the two-dimensional ultrasonicimages. Notably, the deep learning structure of the ultrasonic system 10according to an embodiment of the present invention is not limited tothe U-Net network structure; other structures which may detect theultrasonic image object in the two-dimensional ultrasonic images areapplicable to the present invention.

Since a computation of the U-Net network structure for segmenting theultrasonic image object in the two-dimensional ultrasonic images is toohigh, the ultrasonic system 10 according to an embodiment of the presentinvention proportionally shrinks the deep learning structure and embedsthe deep learning structure into a machine of the ultrasonic system 10to finish the positioning step of the ultrasonic image object.

In step 208, the ultrasonic system 10 is configured to determine thecontour and the location of the ultrasonic image object based on thefirst image of each of the two-dimensional ultrasonic images obtained instep 206. In an embodiment, the ultrasonic system 10 is configured todetermine the preliminary contour and the location of the ultrasonicimage object based on a contour determination method 30. In detail,refer to FIG. 3, which is a schematic diagram of the contourdetermination method 30 according to an embodiment of the presentinvention. The contour determination method 30 includes the followingsteps:

Step 302: Start.

Step 304: Obtain the first image of each of the two-dimensionalultrasonic images.

Step 306: Perform histogram equalization on the first image of each ofthe two-dimensional ultrasonic images.

Step 308: Determine the location and a range of the first image of eachof the two-dimensional ultrasonic images after the histogramequalization and of the deep learning structure for semanticsegmentation.

Step 310: Determine the contour of the ultrasonic image object based onan activation function and/or a binary threshold for the first image ofeach of the two-dimensional ultrasonic images after the histogramequalization and of the deep learning structure for semanticsegmentation.

Step 312: End.

In order to precisely determine the contour and the location of theultrasonic image object in the two-dimensional ultrasonic images, theultrasonic system 10 according to an embodiment of the present inventionmay determine the contour of the ultrasonic image object based on twodifferent methods of the contour determination method 30. Instep 304,the first image is obtained based on the deep learning structure,wherein the preliminary contour of the object is included in the firstimage. In step 306, the first image of each of the two-dimensionalultrasonic images is performed with the histogram equalization toincrease a contrast of the first image. In step 308, the deep learningstructure for semantic segmentation is utilized for determining thelocation and the range of the object in the first image of each of thetwo-dimensional ultrasonic images after the histogram equalization.Then, in step 310, the contour of the ultrasonic image object isdetermined with the activation function and/or the binary threshold forthe first image of each of the two-dimensional ultrasonic images afterthe deep learning structure for semantic segmentation, wherein theactivation function determines the contour and the location of theultrasonic image object by expanding the contour of the ultrasonic imageobject outwardly, which is obtained in step 210; the binary thresholddetermines the contour and the location of the ultrasonic image objectaccording to the contour of the ultrasonic image object obtained in step210.

Notably, the above contour determination method 30 may simultaneouslyadopt the activation function and the binary threshold to determine thecontour and the location of the ultrasonic image object. Alternatively,in another embodiment, the ultrasonic system 10 according to anembodiment of the present invention may determine the contour and thelocation of the ultrasonic image object according to the activationfunction or the binary threshold, which is within the scope of thepresent invention.

Regarding the method of determining the contour and the location of theultrasonic image object based on the activation function, please referto FIG. 4, which is a schematic diagram of a contour determinationmethod 40 according to an embodiment of the present invention. Thecontour determination method 40 determines the contour and the locationof the ultrasonic image object based on the activation function, whichincludes the following steps:

Step 402: Start.

Step 404: Set an edge threshold of the first image.

Step 406: Perform inverse Gaussian gradient on the first image.

Step 408: Detect an edge of the first image.

Step 410: Generate a circle inside the preliminary contour of theultrasonic image object based on the preliminary contour and thelocation of the ultrasonic image object of the first image, and expandthe circle outwardly till the circle reaches the edge threshold of thefirst image.

Step 412: End.

In step 404, the edge threshold of the first image is set as a stoppingpoint when the preliminary contour expands outwardly. In step 406, theinverse Gaussian gradient is performed on the first image to blur thefirst image. In step 408, the edge of the image is detected. In step410, according to the preliminary contour and location of the ultrasonicimage object in the first image, the circle inside the preliminarycontour of the ultrasonic image object is generated and the circle isexpanded outwardly till reaching the edge threshold of the first image,i.e. the stopping point. Therefore, the ultrasonic system 10 accordingto an embodiment of the present invention may determine the contour andthe location of the ultrasonic image object based on the preliminarycontour of the ultrasonic image object and the contour determinationmethod 40.

On the other hand, regarding the method of determining the contour andthe location of the ultrasonic image object based on the binarythreshold, please refer to FIG. 5, which is a schematic diagram of acontour determination method 50 according to an embodiment of thepresent invention. The contour determination method 50 determines thecontour and the location of the ultrasonic image object method based onthe binary threshold, which includes the following steps:

Step 502: Start.

Step 504: Determine the binary threshold of the first image.

Step 506: Determine the contour of the ultrasonic image object accordingto the binary threshold, the preliminary contour and the location of theultrasonic image object of the first image.

Step 508: End.

Based on the contour determination method 50, the ultrasonic system 10determines the binary threshold of the first image in step 504, e.g. an8-bit image has a grayscale value 255. In step 506, the first image isdivided into two colors (e.g. black and white) based on the binarythreshold, the contour of the ultrasonic image object is determinedaccording to the preliminary contour and location of the ultrasonicimage object of the first image after the histogram equalization. In anembodiment, when the ultrasonic image object is a bladder, and thegrayscale value of the binary threshold of the 8-bit image is 128,pixels in the first image with grayscale value over 128 are classifiedas a bladder and pixels with grayscale value lower than 128 areclassified as not a bladder, such that the contour determination method50 may distinguish the bladder in the first image (i.e. the ultrasonicimage object) and compare with the preliminary contour of the ultrasonicimage object. Therefore, the ultrasonic system 10 according to anembodiment of the present invention may determine the contour and thelocation of the ultrasonic image object based on the preliminary contourof the ultrasonic image object and the contour determination method 50.

Notably, domain parameters of the above contour determination methods40, 50 are determined based on an exhaustive method, such that anoptimal domain parameter of the ultrasonic image object is determined.

After the contour of the ultrasonic image object corresponding to eachof the two-dimensional ultrasonic images is determined in step 208, thethree-dimensional model corresponding to the ultrasonic image object isreconstructed based on the contour of the ultrasonic image objectdetermined in step 210. Regarding steps of reconstructing thethree-dimensional model corresponding to the ultrasonic image object,please refer to FIG. 6, which is a schematic diagram of athree-dimensional modeling method 60 according to an embodiment of thepresent invention. The three-dimensional modeling method 60 includes thefollowing steps:

Step 602: Start.

Step 604: Combine the ultrasonic image objects as a three-dimensionalimage via a scanning method according to the two-dimensional ultrasonicimages and the offset angle, the rotation axis and the frequencycorresponding to the two-dimensional ultrasonic images.

Step 606: Establish a three-dimensional slice model based on thethree-dimensional image.

Step 608: Establish the three-dimensional model of the ultrasonic imageobject via a three-dimensional internal interpolation method based onthe three-dimensional slice model.

Step 610: Determine a maximal three-dimensional slice corresponding tothe ultrasonic image object from the three-dimensional slice model.

Step 612: Finish the three-dimensional model by expanding outwardly ofthe ultrasonic image object based on the three-dimensional slice model.

Step 614: End.

In step 604, the ultrasonic system 10 combines multiple two-dimensionalultrasonic images, which are of a sequence, as the three-dimensionalimage via the scanning method according to each first image includingthe ultrasonic image object obtained in step 206 and the correspondingrotation axis and the frequency corresponding to the probe 102. In anembodiment, the scanning method may be a sector scan or a sagittal scan,which combines consecutive ultrasonic image objects as thethree-dimensional ultrasonic images. In other words, the ultrasonicsystem 10 may establish the three-dimensional ultrasonic image based onmultiple two-dimensional ultrasonic images including the ultrasonicimage objects, of one sequence (e.g. 50 images), the offset angle, therotation axis, the frequency corresponding to the probe 102 and aformula (1) , wherein the formula (1) projects Y-axis of the ultrasonicimage object onto Z-axis. And the formula (1) is:

$\begin{matrix}{{\sum\limits_{i = 0}^{EndofSequenceImage}z_{i}} = {{\sum\limits_{i = 0}^{EndofSequenceImage}640} + \left( {\cos\;{Degree}_{i}*{objectDown}_{i}} \right)}} & (1)\end{matrix}$

In formula (1), i denotes i-th ultrasonic image object of the sequence,640 denotes horizontal pixel value of resolution, Degree_(i) denotes theoffset angle when the probe 102 performs scanning, objectDown_(i)denotes a lower section of a bottom plane of the ultrasonic imageobject. Notably, the horizontal resolution of the two-dimensional imageof the ultrasonic system 10 is not limited to 640.

In step 606, the three-dimensional slice model is established based onthe three-dimensional ultrasonic image, i.e. the three-dimensionalultrasonic image is sliced into multiple three-dimensional slices. Then,in step 608, the three-dimensional model of the ultrasonic image objectis established with the three-dimensional internal interpolation methodbased on the three-dimensional slice model, which makes up missing partsof the ultrasonic image object, such that a complete ultrasonic imageobject model is finished.

In an embodiment, the ultrasonic system 10 may calculate a maximaldistance between two three-dimensional slices and perform thethree-dimensional internal interpolation method on the twothree-dimensional slices. FIG. 7 is a schematic diagram of thethree-dimensional inner interpolation performed on the ultrasonic imageobject according to an embodiment of the present invention. In athree-dimensional space, the ultrasonic system 10 may utilize theleft-most and the right-most three-dimensional slices to inwardlyperform the three-dimensional internal interpolation method to establishthe ultrasonic image object model.

However, since the accuracy of the three-dimensional internalinterpolation method may be affected by a scanning speed and a shape ofthe ultrasonic image object (e.g. a shape bladder shape is oval whenunder examination), in step 610, the ultrasonic system 10 determines themaximal three-dimensional slice from the three-dimensional slices, andin step 612, the three-dimensional model is finished based on themaximal three-dimensional slice by expanding the ultrasonic image objectoutwardly, as shown in FIG. 8, which is a schematic diagram of athree-dimensional external interpolation performed on the ultrasonicimage object according to an embodiment of the present invention, i.e.expanding the maximal three-dimensional slice outwardly in the middle ofFIG. 8 to finish the three-dimensional model.

Therefore, the three-dimensional model of the ultrasonic image objectmay be accurately established based on the three-dimensional modelingmethod 60, such that the ultrasonic system 10 may determine the volumeof the ultrasonic image object based on the three-dimensional modelcorresponding to the ultrasonic image object determined in step 212.

The above embodiments illustrate that a volume acquisition method for anobject in ultrasonic image and related ultrasonic system of the presentinvention may detect the ultrasonic image object via the deep learningstructure to accurately and efficiently calculate the volume of theultrasonic image object by establishing the three-dimensional model. Inaddition, according to different requirements, volume acquisition methodfor an object in ultrasonic image and related ultrasonic system of thepresent invention may be utilized for calculating image volume ofcomputed tomography (CT) system or magnetic resonance imaging (MRI)system.

In summary, embodiments of the present invention provides a volumeacquisition method for an object in ultrasonic image and relatedultrasonic system, which combines the deep learning structure andestablishes the three-dimensional model to accurately and efficientlycalculate a volume of the ultrasonic image object and improve theaccuracy of detection.

Those skilled in the art will readily observe that numerousmodifications and alterations of the device and method may be made whileretaining the teachings of the invention. Accordingly, the abovedisclosure should be construed as limited only by the metes and boundsof the appended claims.

What is claimed is:
 1. An object volume acquisition method of anultrasonic image, for a probe of an ultrasonic system, wherein thevolume acquisition method of the object in the ultrasonic imagecomprises: collecting, by the probe, a plurality of two-dimensionalultrasonic images; obtaining the plurality of two-dimensional ultrasonicimages, an offset angle, a rotation axis and a frequency of the probecorresponding to the plurality of two-dimensional ultrasonic images;segmenting a first image including an ultrasonic image object from eachtwo-dimensional ultrasonic image of the plurality of two-dimensionalultrasonic images based on a deep learning structure; determining acontour of the ultrasonic image object according to the first imagecorresponding to the each two-dimensional ultrasonic image;reconstructing a three-dimensional model corresponding to the ultrasonicimage object according to the contour of the ultrasonic image objectcorresponding to the each two-dimensional ultrasonic image; andcalculating a volume of the ultrasonic image object according to thethree-dimensional model corresponding to the ultrasonic image object. 2.The object volume acquisition method in the ultrasonic image of claim 1,wherein the deep learning structure is a U-Net network structure fordetermining a preliminary contour and a location of the ultrasonic imageobject of the first image according to the offset angle, the rotationaxis and the frequency of the probe corresponding to the plurality oftwo-dimensional ultrasonic images.
 3. The object volume acquisitionmethod in the ultrasonic image of claim 2, wherein the step ofdetermining the contour of the ultrasonic image object according to thefirst image corresponding to the each two-dimensional ultrasonic imageincludes: setting an edge threshold; detecting an edge of the firstimage; and generating a circle inside the preliminary contour of theultrasonic image object based on the preliminary contour and thelocation of the ultrasonic image object of the first image, andexpanding the circle outwardly till the circle reaches the edgethreshold of the first image.
 4. The object volume acquisition method inthe ultrasonic image of claim 3, wherein the first image is performedwith histogram equalization before setting the edge threshold of thefirst image.
 5. The object volume acquisition method in the ultrasonicimage of claim 2, wherein the step of determining the contour of theultrasonic image object according to the first image corresponding tothe each two-dimensional ultrasonic image includes: performing histogramequalization on the first image; determining a binary threshold of thefirst image; and determining the contour of the ultrasonic image objectaccording to the binary threshold, the preliminary contour and thelocation of the ultrasonic image object of the first image.
 6. Theobject volume acquisition method in the ultrasonic image of claim 1,wherein the step of reconstructing the three-dimensional modelcorresponding to the ultrasonic image object according to the contour ofthe ultrasonic image object corresponding to the each two-dimensionalultrasonic image includes: combining the plurality of ultrasonic imageobjects as a three-dimensional image via a scanning method according tothe plurality of two-dimensional ultrasonic images and the offset angle,the rotation axis and the frequency corresponding to the plurality oftwo-dimensional ultrasonic images; establishing a three-dimensionalslice model based on the three-dimensional image; and establishing thethree-dimensional model of the ultrasonic image object via athree-dimensional internal interpolation method based on thethree-dimensional slice model.
 7. The object volume acquisition methodin the ultrasonic image of claim 6, wherein the step of establishing thethree-dimensional model of the ultrasonic image object via thethree-dimensional internal interpolation method based on thethree-dimensional slice model includes: determining a maximalthree-dimensional slice corresponding to the ultrasonic image objectfrom the three-dimensional slice model; and finishing thethree-dimensional model by expanding outwardly of the ultrasonic imageobject based on the three-dimensional slice model.
 8. An ultrasonicsystem, for calculating a volume of ultrasonic image object, comprising:a probe, configured to collect a plurality of two-dimensional ultrasonicimages; and a processor, configured to obtain the plurality oftwo-dimensional ultrasonic images, an offset angle, a rotation axis anda frequency of the probe corresponding to the plurality oftwo-dimensional ultrasonic images, segment a first image including anultrasonic image object from each two-dimensional ultrasonic image ofthe plurality of two-dimensional ultrasonic images based on a deeplearning structure, determine a contour of the ultrasonic image objectaccording to the first image corresponding to the each two-dimensionalultrasonic image, reconstruct a three-dimensional model corresponding tothe ultrasonic image object according to the contour of the ultrasonicimage object corresponding to the each two-dimensional ultrasonic image,and calculate a volume of the ultrasonic image object according to thethree-dimensional model corresponding to the ultrasonic image object. 9.The ultrasonic system of claim 8, wherein the deep learning structure isa U-Net network structure for determining a preliminary contour and alocation of the ultrasonic image object of the first image according tothe offset angle, the rotation axis and the frequency of the probecorresponding to the plurality of two-dimensional ultrasonic images. 10.The ultrasonic system of claim 9, wherein the processor is configured toset an edge threshold, detect an edge of the first image, generate acircle inside the preliminary contour of the ultrasonic image objectbased on the preliminary contour and the location of the ultrasonicimage object of the first image, and expand the circle outwardly tillthe circle reaches the edge threshold of the first image.
 11. Theultrasonic system of claim 10, wherein before the edge threshold of thefirst image is set, the processor is configured to perform histogramequalization on the first image.
 12. The ultrasonic system of claim 9,wherein the processor is configured to perform histogram equalization onthe first image, determine a binary threshold of the first image anddetermine the contour of the ultrasonic image object according to thebinary threshold, the preliminary contour and the location of theultrasonic image object of the first image.
 13. The ultrasonic system ofclaim 8, wherein the processor is configured to combine the plurality ofultrasonic image objects as a three-dimensional image via a scanningmethod according to the plurality of two-dimensional ultrasonic imagesand the offset angle, the rotation axis and the frequency correspondingto the plurality of two-dimensional ultrasonic images, establish athree-dimensional slice model based on the three-dimensional image, andestablish the three-dimensional model of the ultrasonic image object viaa three-dimensional interpolation method based on the three-dimensionalslice model.
 14. The ultrasonic system of claim 13, wherein theprocessor is configured to determine a maximal three-dimensional slicecorresponding to the ultrasonic image object from the three-dimensionalslice model, and finish the three-dimensional model by expandingoutwardly of the ultrasonic image object based on the three-dimensionalslice model.